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Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies

Author

Listed:
  • Andreas Komninos

    (University of Patras
    Diophantos Computer Technology Institute and Press)

  • Charalampos Kostopoulos

    (OptionsNet IT Services and Consulting, Optionsnet)

  • John Garofalakis

    (University of Patras
    Diophantos Computer Technology Institute and Press)

Abstract

Sailing holiday activities represent a significant portion of the Blue Economy growth in Europe and across the world. Due to the global financial crisis, yacht ownership has declined, but demand for such holiday products remained steady, therefore shifting the yachters profile towards younger and less experienced consumers who prefer to charter boats, rather than own one. Boat chartering offers more flexibility to explore different regions from year to year, but this means that significantly more time must be spent planning the route, since local experience is absent. The tourists’ experience during the initial contemplation and planning phase, taking place weeks or months before an actual trip, and where a broad range of route options needs to be explored, could thus significantly benefit from support given by automated IT tools. Current literature demonstrates a complete lack of research in the development of itinerary recommendation systems in the context of sailing holidays. In this paper, we describe a methodology for the automatic generation of route recommendations, based on the semantic modelling of spatial data, and the determination of realistic sea route options, based on vessel density maps produced from raw AIS data. We demonstrate the implementation and results from this methodology using one of the most popular sailing regions of Greece, namely the Ionian Sea, as a case study.

Suggested Citation

  • Andreas Komninos & Charalampos Kostopoulos & John Garofalakis, 2022. "Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies," Information Technology & Tourism, Springer, vol. 24(2), pages 265-298, June.
  • Handle: RePEc:spr:infott:v:24:y:2022:i:2:d:10.1007_s40558-022-00224-x
    DOI: 10.1007/s40558-022-00224-x
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    References listed on IDEAS

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    1. Dominik Filipiak & Krzysztof Węcel & Milena Stróżyna & Michał Michalak & Witold Abramowicz, 2020. "Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(5), pages 435-450, October.
    2. Stefan Kuhlemann & Kevin Tierney, 2020. "A genetic algorithm for finding realistic sea routes considering the weather," Journal of Heuristics, Springer, vol. 26(6), pages 801-825, December.
    3. Gunawan, Aldy & Lau, Hoong Chuin & Vansteenwegen, Pieter, 2016. "Orienteering Problem: A survey of recent variants, solution approaches and applications," European Journal of Operational Research, Elsevier, vol. 255(2), pages 315-332.
    4. Stefan Kuhlemann & Kevin Tierney, 2020. "Correction to: A genetic algorithm for finding realistic sea routes considering the weather," Journal of Heuristics, Springer, vol. 26(6), pages 827-827, December.
    5. Pan Sheng & Jingbo Yin, 2018. "Extracting Shipping Route Patterns by Trajectory Clustering Model Based on Automatic Identification System Data," Sustainability, MDPI, vol. 10(7), pages 1-13, July.
    6. Berta Ferrer-Rosell & Germa Coenders & Estela Marine-Roig, 2017. "Is planning through the Internet (un)related to trip satisfaction?," Information Technology & Tourism, Springer, vol. 17(2), pages 229-244, June.
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